migration framework for decentralized and proactive risk identification …€¦ · ·...
TRANSCRIPT
5th International Symposium and 27th National Conference on Operation Research
Piraeus University of Appluied Sciences (Technological Education Institute of Piraeus)
Aigaleo - Athens on June 9-11, 2016
Page 85
Migration framework for decentralized and proactive risk
identification in a Steel Supply Chain via Industry 4.0 technologies
Florian Schlüter Graduate School of Logistics,
Technical University of Dortmund
Philipp Sprenger Fraunhofer-Institute for Material Flow and Logistics IML
Abstract
For competitive risk mitigation in future steel supply chain networks a decentralized risk identification corresponding to
the 4th Industrial Revolution is required. By integrating Cyber-Physical-Systems (CPS) in supply chain networks, a large
number of real-time data in different formats will be available and must be consolidated. A framework is missing that
leads to an improved risk identification and mitigation process by linking risks and CPS. In this paper a framework will be
presented which correlates identified supply chain risks, suitable Industry 4.0 (I4.0) technologies and relevant finance
indicators. The framework enables to identify relevant risks and their potential impact on companies’ assets and
working capital, and also gives advice how these risks can be registered in advance by using I4.0 technologies.
KEYWORDS
Supply Chain Risk Management, Cyber-Physical-Systems, Industry 4.0, Steel Industry
1. INTRODUCTION
Steel manufacturers are an inseparable part of several complex supply chain (SC) networks and deliver key
resources for different manufacturing industries such as automotive, machineries and building companies.
Operating this complex SC network is accompanied by several operational risks [1]. To ensure SC security as
well as to support the competitiveness of steel companies and their customers it is necessary to identify
potential risks in steel SCs in advance. But in reality insufficient IT networks between SC partners lead to
unstable and prone transport chains. This leads to storage and transport bottlenecks and therefore to
expensive ad-hoc solutions [2] like additionally rented transport capacity. A SC wide proactive risk
management based on risk related information transparency is required to increase the security of supply,
decrease safety stocks and lower costs for steel manufacturer and their customers [2, 3]. The potential
advantages from a proactive supply chain risk management (SCRM) based on information transparency and
corresponding to the 4th
Industrial Revolution have been recognized by Zweig et al. [4]. In their study the
authors state that disruptions in steel SC can have a serious impact on the companies’ competitiveness and
threaten their existence. A digitalized SC (including Machine-to-Machine-Communication) makes potential
risks visible and allows companies to monitor material flows in real time and to develop future plans with
the help of predictive analytics. A system is essentially needed which enables recognition, analysis and
assessment of negative trends to manage risks inside and outside of the SC [4]. To support the development
of a SC wide risk related information transparency and to make a first step towards a digitalized SCRM, this
publication presents a procedure framework for supporting the migration of I4.0 technologies. The
contribution is broken down into 5 sections. After the introduction in section 1 follows a state of the art
analysis of existing approaches in section 2. Derived from this analysis the developed framework is
introduced in section 3 and partially applied on an example steel SC in section 4. Section 5 summarizes the
findings of this paper and discusses the need for future developments in SCRM.
5th International Symposium and 27th National Conference on Operation Research
Piraeus University of Appluied Sciences (Technological Education Institute of Piraeus)
Aigaleo - Athens on June 9-11, 2016
Page 86
2. RESEARCH OVERVIEW
A generalized and standardized risk management (RM) process was developed by the International
Organization for Standardization [5] and consists of six phases [5, 6]. This generalized RM process is also
suitable for the specified SCRM process [7]. In addition to the standardized process [5, 7] the “risk” is
separated in the components “source”, “event” and “effect” which can be integrated in a cause-and-effect
relationship (network) [8]. In general SCRM methods can be distinguished between qualitative and
quantitative methods [9]. The advantages of qualitative methods like interviews and estimations, lie in less
need of personal resources and available data [9, 10]. However these methods are only suitable for risks
with a low need for assessment accuracy and a low probability of occurrence [9, 10]. Once it comes to risks
with a high occurrence rate, it is necessary to use quantitative methods [9, 10]. In practice quantitative
methods like statistics, optimization models and simulations are not frequently applied, because of lacking
available data [9–11]. Though there is an empirical evidence for the need of more quantitative and
analytical methods as well as special IT support for RM in companies [11]. One step to overcome this lack is
the implementation of I4.0 technologies into supply chain processes. Generating a continuous data basis
along SC partners creates transparency and supports a more quantitative and real time SCRM. More
detailed information about I4.0, its definition and paradigms can be found in further literature [12–15].
3. FRAMEWORK DEVELOPMENT
In this section a framework for digitalizing the risk identification process will be developed. The framework
is based on earlier developments by Kirazli and Moetz [16] and Yüzgülec [7]. Kirazli and Moetz [16] provide
a methodological four-phase concept for the introduction of I4.0 from a RM perspective. But there is a lack
of specific I4.0 technologies and the article has a focus on potential risks through the implementation of I4.0
technologies. Therefore they do not cover the digitalization of general SC risks for a proactive recognition.
With the help of the detailed SCRM process of Yüzgülec [7] the four-phase-concept of Kirazli and Moetz [16]
has been adapted to a five-phase-concept which leads the user through the required steps to derive
strategies for migrating I4.0 technologies for proactive risk identification. The five-phase-concept is
presented in Table 8 with a succeeding description of applicable methods in each phase.
Table 8: Five-Phase-Concept for Industry 4.0 Risk Identification
Phase Objectives Methods
1. Process specification
· Structured description and visualization of the
SC process and its sub processes
· Definition of process condition measures
(PCM) (performance indicators for Phase 5)
· Process inspection
· Process model
· Expert interviews
· Workshops
2. Risk identification · Identification of risks for each process
· Collection methods
· Search methods (analytical/creative)
3. Risk analysis
· Cause-effect analysis of identified risks
(sources, events and effects)
· Determining of direct impact of risks on PCM
· Process model and inspection
· Determination and visualization of risk
causalities
4. Risk digitalization
· Examination of future developments and
applications
· Derivation of migration-strategies for I4.0
technologies (not part of this paper)
· Technology management
Ø Technology scouting and forecasting
Ø Technology strategy and roadmap
5. Cost-benefit-Analysis
(not part of this
paper)
· Assessment of risk impact on SC
costs/performance by using KPIs
· Assessment of potentials of using I4.0 tech.
for SCRM
· Comparison
Ø Impact of risks on KPIs
Ø Impact of tech. on risks (occurrence/loss)
Ø Expenditures for tech. implementation
Within the five phases, different methods have to be applied. For the process specification phase the
process chain instrument (PCI) by Kuhn [17] can be used for visualizing the process chain as well as for
defining process condition measures (PCM) as variables for performance indicators. Kuhn [17] defined five
measures for determining the condition of a single process: inventory, lead time, adherence to delivery
5th International Symposium and 27th National Conference on Operation Research
Piraeus University of Appluied Sciences (Technological Education Institute of Piraeus)
Aigaleo - Athens on June 9-11, 2016
Page 87
dates, capacity and process costs. Other visualization methods can be found at Jungmann and Uygun [18].
But how to link this measures systematically to key performance indicators (KPI)? There are several KPI-
systems in supply chain management which provide indicators for process measuring and corporate
objective evaluation at the same time (see [19–21]). Next to SCOR the Supply Chain Balanced Scorecard
(SCBSC), based on the Balanced Scorecard from Kaplan and Norton [22] offers a set of different KPI’s and
measurement categories. The SCBSC-framework allows an evaluation with the help of five different
evaluation-perspectives: “finance”, “customer”, “internal process”, “supplier” and “development”. In this
contribution the finance-perspective is integrated into the five-phase-framework. Typical corporate finance
objectives are success, liquidity, profitability and the companies value add while reducing the
process/supply chain costs and working capital costs [23]. Potential finance indicators are revenue, earnings
before interest and taxes (EBIT), return on capital employed (ROCE), cash-to-cash-cycle, return on invest
(ROI), economic value added (EVA) and working capital and supply chain costs [23]. Romeike and Hager [24]
present a collection of risk identification methods divided into collection methods for identifying existing
and partially known risks and search methods (analytical and creative) for identifying future risk potentials.
Typical approaches which are also used by Häntsch and Huchzermeier [25] to identify risks in car
manufacturing networks are: risk checklists, expert interviews and workshops. During the risk analysis
phase a cause-effect-analysis for all identified risk sources, events and effects has to be carried out.
Afterwards the direct impact of the initial risk sources on their corresponding process has to be determined.
An approach for risk synthesizing with several methods comes from Yüzgülec [7], based on a methodology
of Lingnau and Jonen [26], as well as methods for determining the direct impact of risk sources on process
condition measures. When the SC risks, their correlation and the impact of their initial sources on the
performance indicators are known, a technology scouting must be executed. A comprehensive collection of
approaches for technology scouting and forecasting from the field of technology management can be found
at Schuh and Klappert [27]. But it is also possible to use existing publications like [14] or [28] as decision
support for choosing I4.0 technologies. The fifth and last phase consists of a cost-benefit-analysis which
evaluates the implementation and use of I4.0 technologies within SCRM and thus gives recommendation
about whether the implementation is worthwhile or not. Methods for technology evaluation are provided
by Schuh and Klappert [27]. Regarding these methods and KPI’s of the SCBSC an assessment of I4.0
technologies in SCRM such as a business case evaluation can be carried out.
4. CONCEPT APPLICATION
In this section four phases of the model will be applied in a use case of a German steel manufacturer.
1. Process specification
For the process specification phase the PCI by Kuhn [17] can be used for visualizing the process chain and
defining PCM for the development of KPIs (see Table 9). In this case an aggregated SC is displayed in Figure
2 with focus on the material flow.
Figure 2: Example steel supply chain
2. Risk identification
Expert interviews at the logistics department of a German steel manufacturer took place, based on
interview methods by Yüzgülec, Romeike and Hager as well as Häntsch and Huchzermeier [7, 24, 25]. The
River
transportHandling Production Storage
Railway
transport
CustomerRaw
material
Cargo Ship
Handling
Lager
Information
Material
Distribution
5th
Inte
rna
tion
al S
ym
po
sium
an
d 2
7th
Na
tion
al C
on
fere
nce
on
Op
era
tion
Re
sea
rch
Pira
eu
s Un
ive
rsity o
f Ap
plu
ied
Scie
nce
s (Te
chn
olo
gica
l Ed
uca
tion
Institu
te o
f Pira
eu
s)
Aig
ale
o - A
the
ns o
n Ju
ne
9-1
1, 2
01
6
P
ag
e 8
8
inte
rview
pa
rtne
rs we
re a
ske
d a
bo
ut ty
pica
l risk e
ven
ts an
d th
eir so
urce
s for h
an
dlin
g, rive
r tran
spo
rt,
railw
ay
tran
spo
rt, wa
reh
ou
sing
an
d d
istribu
tion
pro
cesse
s. Th
e m
ost fre
qu
en
tly m
en
tion
ed
risk fo
r ea
ch
pro
cess w
ill be
use
d fo
r the
risk a
na
lysis (se
e T
ab
le 9
).
3.
Risk
an
aly
sis
Th
e
cho
sen
risk
s a
re
no
t d
irectly
lin
ke
d
to
ea
ch
oth
er.
Th
ere
fore
it
is n
ot
ne
cessa
ry
to
crea
te
a
com
pre
he
nsiv
e ca
use
-an
d-e
ffect re
latio
nsh
ip n
etw
ork
an
d o
nly
the
dire
ct imp
act o
f de
fine
d risk
sou
rces o
n
pro
cess co
nd
ition
me
asu
res (risk
effe
ct) ha
ve b
ee
n d
iscusse
d (se
e T
ab
le 9
). Ho
we
ver g
en
era
lly a
ll ide
ntifie
d
risk so
urce
s, ev
en
ts an
d e
ffects th
rou
gh
ou
t the
de
fine
d p
roce
sses h
ave
to b
e a
na
lyze
d a
nd
corre
late
d (se
e
Ta
ble
9).
4.
Risk
dig
italiza
tion
Fo
r imp
rovin
g risk
ide
ntifica
tion
with
the
he
lp o
f I4.0
, ava
ilab
le te
chn
olo
gie
s ha
ve
to b
e m
atch
ed
to th
e
ide
ntifie
d risk
s. In th
is case
an
existin
g stu
dy
like
[14
] is use
d to
intro
du
ce te
chn
olo
gy
field
s as ca
teg
orie
s,
com
bin
ed
with
exe
mp
lary
risk-re
late
d te
chn
olo
gie
s from
[28
] (see
Ta
ble
9).
Ta
ble
9: In
teg
ratio
n o
f Risk
s, Te
chn
olo
gie
s an
d K
PIs
KPIs
· SC-costs and
working capital
costs with impact
on EBIT and ROCE
· SC-costs and
working capital
costs with impact
on EBIT and ROCE
· SC-costs and
working capital
costs with impact
on EBIT, and ROCE
· Costs SC-costs and
working capital
costs with impact
on EBIT and ROCE
· Working capital
costs with impact
on EBIT and ROCE
· SC-Costs with
impact on EBIT,
revenue and ROCE
Process
Condition
Measure
· (Handling)
Capacity
· (Trans-
port)
Capacity
· (Handling)
Capacity
· Lead time
· Stock
· Costs
Technology Fields
Embedded
Systems
· Smart and
miniature
systems
· Smart and
miniature
systems on
boats
· -
· Smart and
miniature
systems on
trains and
on bridges
· -
· Smart and
miniature
systems on
wagons
Software/ System-
technology
· Simulation
· Pattern recognition
· Digital maintenance
protocols
· Digital BOM
· Simulation
· Pattern recognition
· Big-Data storage and
analytics
· Web Services
(Weather/ News)
· Digital maps/
schedules
· Simulation
· Pattern recognition
· Web Services
(Weather)
· Simulation
· Pattern recognition
· Big-Data storage and
analytics
· Web Services
(Weather/ News)
· Digital maps/
schedules
· Simulation
· Pattern recognition
· Simulation
· Pattern recognition
· Web Services
(Weather)
Sensors
· Vibration
· Weight
· Temperature
· Humidity
· Movements
· Position (GPS)
· Temperature
· Draft
· Position (GPS)
· Tide
· Temperature
· Position (GPS)
· Humidity
· Belt speed
· Temperature
· Vibration
· Position (GPS)
· Humidity
· Weight
· Temperature
· Vibration
· Weight
· Movements
· Temperature
· Position (GPS)
· Humidity
· Weight
Human-
Machine
Interface
· Live-
tracking
of crane
controls
· -
· -
· Live-
tracking
of train
controls
· Live-
tracking
of crane
controls
· -
5th International Symposium and 27th National Conference on Operation Research
Piraeus University of Appluied Sciences (Technological Education Institute of Piraeus)
Aigaleo - Athens on June 9-11, 2016
Page 89
Co
mm
un
i-
cati
on
·
Re
al ti
me
·
Wir
ele
ss
·
Sta
tio
na
ry
·
Re
al t
ime
·
Wir
ele
ss
·
Sta
tio
na
ry
/Mo
bile
·
Wir
ed
·
Sta
tio
na
ry
·
Re
al ti
me
·
Wir
ele
ss
·
Sta
tio
na
ry
/Mo
bile
·
Re
al ti
me
·
Wir
ed
·
Re
al ti
me
·
Wir
ele
ss
·
Mo
bile
Ris
k s
ou
rce
·
Co
mp
on
en
t
bre
akd
ow
n
·
Low
tid
e d
ue
to lo
ng
te
rm
hig
h
tem
pe
ratu
re
·
Fro
zen
com
po
ne
nts
du
e t
o lo
w
tem
pe
ratu
re
·
Ra
ilwa
y b
rid
ge
bro
ke
n d
ue
to
ob
sole
sce
nce
·
Co
il b
asi
ns
bro
ke
n d
ue
to
ob
sole
sce
nce
·
Co
nd
en
sate
wa
ter
Ris
k e
ve
nt
·
Cra
ne
is
blo
cke
d
·
Re
du
ced
loa
d
cap
aci
ty
·
Be
lt
con
veyo
r
blo
cke
d
·
Ra
ilwa
y
blo
cke
d
·
Re
du
ced
sto
rag
e
cap
aci
ty
·
Co
ils
ge
t
da
ma
ge
d
(ru
st)
Pro
cess
Ha
nd
ling
(Se
a p
ort
)
Riv
er
tra
nsp
ort
Ha
nd
ling
(Pla
nt
po
rt)
Ra
ilwa
y
tra
nsp
ort
Sto
rag
e
Dis
-
trib
uti
on
Based on these recommendations digitalization strategies can be derived. Afterwards these strategies have
to be assessed within a cost-benefit-analysis.
5. CONCLUSION AND OUTLOOK
The objective of this paper was the development of a framework to support the migration of I4.0
technologies into SCs for an improved risk identification. Therefore the authors presented a brief research
overview regarding I4.0 and SCRM. Based on that a framework has been developed. The framework
consists of five-phases with different methods in each phase. The developed framework has been applied to
a use case of a German steel manufacturer to show the applicability. One of the implications for further
research is to add a cost based assessment to the suggested method of Yüzgulec [7] as well as a
methodology to model the advantages of the I4.0 technologies in the simulation. Another implication is to
find other suitable assessment methods, depending on more or less detailed requirements by the user. The
developed framework only considers the risk identification phase of the SCRM process. Further
development is needed to extend the framework to the risk assessment and control phase. There is also a
need for further research regarding the applicability to other use cases and regarding the deviation of
digitalization strategies.
REFERENCES
1. Rotering J, von Hochberg P, Naujok N, Schmidt-Brockhoff T (2012) Die Stahlindustrie in Deutschland:
"Rückgrad des Industriestandorts Deutschland"
2. Vastag A, Rathjens M, Wiedenbruch A (2013) Entwicklung eines Konzeptes für sichere und robuste
Transportkette zur Gewährleistung der Versorgung in der Stahlindustrie. In: Wolf-Kluthausen H (ed)
Jahrbuch Logistik 2013. free beratung, Korschenbroich, pp 38–41
3. Kümmerlein R (2011) Raus aus dem starren Stahlnetz. Forschungs Agenda Logistik(1): 34–36
4. Zweig M, Agawal A, Stall B, Bremer C, Mangers P, Beifus A, Chauhan M (2015) Globalize or
customice: finding the right balance: Global steel 2015-2016
5. ISO (2009) Guide 73: Risk management - Vocabulary
6. Weis U (2009) Risikomanagement nach ISO 31000: Risiken erkennen und erfolgreich steuern.
Grundlagen und Einführung in das Thema Risikomanagement; Inhalt der ISO 310000: der
5th International Symposium and 27th National Conference on Operation Research
Piraeus University of Appluied Sciences (Technological Education Institute of Piraeus)
Aigaleo - Athens on June 9-11, 2016
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Geltungsbereich und die wesentlichen Inhalte; Risikobewertungsmethoden. WEKA-Praxislösungen.
WEKA-Media, Kissing
7. Yüzgülec G (2015) Aggregierte Bewertung von Risikoursachen in Supply Chains der
Automobilindustrie. Verlag Praxiswissen, Dortmund
8. Shin K, Shin Y, Kwon J, Kang S (2012) Development of risk based dynamic backorder replenishment
planning framework using Bayesian Belief Network. Computers & Industrial Engineering 62(3): 716–
725. doi: 10.1016/j.cie.2011.11.015
9. Ziegenbein A (2007) Supply Chain Risiken: Identifikation, Bewertung und Steuerung. vdf
Hochschulverlag AG, Zürich
10. von Pfuhlstein HH (2008) Qualitatives Risikomanagement in der Supply Chain, vol 17863, Zürich
11. Romeike F, Eicher A, Köcher A, Meissner J (2013) Chancen-/ Risiko-Radard 2013: Status Quo einer
Risiko- und Chancenorientierten Unternehmenssteuerung
12. (2014) Industrie 4.0: Whitepaper FuE-Themen
13. Roth A (2016) Industrie 4.0 - Hype oder Revolution? In: Roth A (ed) Einführung und Umsetzung von
Industrie 4.0: Grundlagen, Vorgehensmodell und Use Cases aus der Praxis. Springer-Verlag, Berlin,
Heidelberg, pp 1–15
14. Bischoff J, Taphorn C, Wolter D, Braun N, Fellbaum M, Goloverov A, Ludwig S, Hegmanns T, Prasse C,
Henke M, ten Hompel M, Döbbeler F, Fuss E, Kirsch C, Mättig B, Braun S, Guth M, Kaspers M, Scheffler
D (2015) Erschließen der Potenziale der Anwendung von 'Industrie 4.0' im Mittelstand: Studie im
Auftrag des Bundesministeriums für Wirtschaft und Energie (BMWi), Mülheim an der Ruhr
15. Siepmann D (2016) Industrie 4.0 - Fünf zentrale Paradigmen. In: Roth A (ed) Einführung und
Umsetzung von Industrie 4.0: Grundlagen, Vorgehensmodell und Use Cases aus der Praxis. Springer-
Verlag, Berlin, Heidelberg, pp 35–46
16. Kirazli A, Moetz A (2015) A methodological approach for evaluating the influences of Industrie 4.0 on
risk management of the goods receiving area in a German automotive manufacturer. In: 22nd
EurOMA Conference: Operations Management for Sustainable Competitiveness
17. Kuhn A (1995) Prozessketten in der Logistik: Entwicklungstrends und Umsetzungsstrategien. Verlag
Praxiswissen, Dortmund
18. Jungmann T, Uygun Y (2009) Das Dortmunder Prozesskettenmodell in der Instralogistik. In: Bandow G,
Holzmüller HH (eds) „Das ist gar kein Modell!“: Unterschiedliche Modelle und Modellierungen in
Betriebswirtschaftslehre und Ingenieurwissenschaften. Gabler Verlag / Springer Fachmedien
Wiesbaden GmbH Wiesbaden, Wiesbaden
19. Kaplan RS, Norton DP (1996) The Balanced Scorecard - Translating Strategy into Action. Harvard
Business Press, Boston
20. Keller M, Hellingrath B Kennzahlenbasierte Wirtschaftlichkeitsbwertung in Produktions- und
Logistiknetzwerken. In: Otto A, Obermaier R (eds) Logistikmanagement: Analyse, Bewertung und
Gestaltung logistischer Systeme. Deutscher Universitäts-Verlag, Wiesbaden, pp 51–76
21. Poluha RG (2010) Quintessenz des Supply Chain Managements: Was Sie wirklich über Ihre Prozesse in
Beschaffung, Fertigung, Lagerung und Logistik wissen müssen. Springer, Berlin
22. VDI 4400 (2002) Logistikkennzahlen für die Distribution(4400)
23. Werner H Supply Chain Management: Grundlagen, Strategien, Instrumente und Controlling. Gabler
Verlag, Wiesbaden
24. Romeike F, Hager P (2013) Erfolgsfaktor Risiko-Management 3.0: Methoden, Beispiele, Checklisten.
Praxishandbuch für Industrie und Handel. Springer Gabler, Wiesbaden
25. Häntsch M, Huchzermeier A (2013) Identifying, analyzing, and assessing risk in the strategic planning
of a production network: the pracitcal view of a German car manufacturer. Journal Management
Control 24: 125–158
26. Lingnau V, Jonen A (2007) Kognitionsorientiertes Risikocontrolling in der Supply Chain: Supply Chain
Risk Map. In: Vahrenkamp R, Siepermann C (eds) Risikomanagement in Supply Chains: Gefahren
abwehren, Chancen nutzen, Erfolg generieren. Erich Schmidt Verlag, Berlin
27. Schuh G, Klappert S (2011) Technologiemanagement: Handbuch Produktion und Management 2. VDI-
Buch. Springer-Verlag Berlin Heidelberg, Berlin Heidelberg
5th International Symposium and 27th National Conference on Operation Research
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28. Rozados IV, Tjahjono B (2014) Big Data Analytics in Supply Chain Management: Trends and Related
Research
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Committees
Conference Chairs Athanasios Spyridakos Piraeus University of Applied Sciences, Greece Lazaros Vryzides, Piraeus University of Applied Sciences, Greece
Scientific Committee Aksen D., Koc University (Turkey) Alexopoulos, S., Hellenic Gas Transmission System Operator (Greece) Anagnostopoulos, K., Democritus University of Thrace (Greece) Arabatzis, G., Democritus University of Thrace (Greece) Askounis, D., National Technical University of Athens (Greece) Assimakopoulos, V., National Technical University of Athens (Greece) Bojovic, N., University of Belgrade (Serbia) Burnetas, A., University of Athens (Greece) Capros, P., National Technical University of Athens (Greece) Carayannis, E., George Washington University (USA) Chalikias, M., Piraeus University of Applied Sciences (Greece) Conejo, A., University of Ohio (USA) Daras, N., Hellenic Army Academy (Greece) Delias, P., Technological Educational Institute of Kavala (Greece) Diakoulaki, D., National Technical University of Athens (Greece) Doukas, H., National Technical University of Athens (Greece) Doumpos, M., Technical University of Crete (Greece) Dounias, G., University of the Aegean (Greece) Economou, A., University of Athens (Greece) Figueira, J., Technical University of Lisbon (Portugal) Flamos, Α., University of Piraeus (Greece) Georgiadis, M.C., Aristotle University of Thessaloniki (Greece) Giannakopoulos, D., Piraeus University of Applied Sciences (Greece) Goletsis, G., University of Ioannina (Greece) Golias, M., Memphis University, (USA) Grigoroudis E., Technical University of Crete (Greece) Hurson, C., University of Rouen (France) Ierapetritou, M., Rutgers University, NJ (USA) Ioannidis, S. Technical University of Crete (Greece) Jouini, O. Ecole Centrale Paris (France) Kalfakakou, G., Aristotle University of Thessaloniki (Greece) Kanellos, F., Technical University of Crete (Greece) Kostoglou, V., Technological Educational Institute of Thessaloniki (Greece) Kouikoglou, V.S. Technical University of Crete (Greece) Kozanidis, G., University of Thessaly (Greece) Kyriakidis, E.G., Athens University of Economics and Business (Greece) Leopoulos, V., National Technical University of Athens (Greece) Liberopoulos, G., University of Thessaly (Greece) Loizidou, M., National Technical University of Athens (Greece) Magirou, E.F., Athens University of Economics and Business (Greece) Malamis, D., National Technical University of Athens (Greece) Manolitzas, P., University of Bath (UK) Manos, V., Aristotle University of Thessaloniki (Greece) Manthou, V., University of Macedonia (Greece) Matsatsinis, N., Technical University of Crete (Greece) Mavrotas, G., National Technical University of Athens (Greece) Mentzas, G., National Technical University of Athens (Greece) Migdalas, A., Lulea University of Technology (Sweden) & Aristotle University of Thessaloniki (Greece) Milenković, M., University of Belgrade (Serbia)
5th International Symposium and 27th National Conference on Operation Research
Piraeus University of Appluied Sciences (Technological Education Institute of Piraeus)
Aigaleo - Athens on June 9-11, 2016
Page 4
Minoux, M., University Paris 6 (France) Moustakas, K., National Technical University of Athens (Greece) Mpourouzian, M., National Technical University of Athens (Greece) Panagiotopoulos, J.C., University of Piraeus (Greece) Pandelis, D., University of Thessaly (Greece) Papachristos S, University of Ioannina, (Greece) Papadopoulos, C.T., Aristotle University of Thessaloniki (Greece) Papageorgiou, M., Technical University of Crete (Greece) Papajorgji, P., Canadian Institute of Technology (Albania) Papamichail, N., University of Manchester (UK) Papathanasiou, J., University of Macedonia (Greece) Paravantis, J., University of Piraeus (Greece) Pardalos, P., University of Florida (USA) Paschos, V., University of Paris Dauphine (France) Phillis, Y., Technical University of Crete (Greece) Politis, I., Region of Attica (Greece) Psaromiligkos, I., Piraeus University of Applied Sciences (Greece) Psarras, J., National Technical University of Athens (Greece) Sabrakos, E., University of Piraeus (Greece) Saharidis, G.K.D., University of Thessaly (Greece) Sahin, E., Ecole Centrale Paris (France) Salmon, I., Piraeus University Applied Sciences (Greece) Samaras, N., University of Macedonia (Greece) Samouilidis, I.E., National Technical University of Athens (Greece) Siskos, Y., University of Piraeus (Greece) Skouri, K., University of Ioannina (Greece) Slowinski, R., Poznan University of Technology (Poland) Spyridakos, A., Piraeus University of Applied Sciences (Greece) Tagaras, G., Aristotle University of Thessaloniki (Greece) Tarantilis, C., Athens University of Economics & Business (Greece) Tatsiopoulos, I., National Technical University of Athens (Greece) Theodoridis, Y., University of Piraeus (Greece) Thomaidis, N., University of the Aegean (Greece) Tsotsolas, N., Piraeus University of Applied Sciences (Greece) Tsoukala, V., National Technical University of Athens (Greece) Tsoukias, A., Universite Paris Dauphine (France) Tsouros, C., Aristotle University of Thessaloniki (Greece) Vlachopoulou, M., University of Macedonia (Greece) Xidonas, P., National Technical University of Athens (Greece) Ypsilantis, P., Technological Educational Institute of Larissa (Greece) Zazanis, M., Athens University of Economics and Business (Greece) Ziliaskopoulos, A., University of Thessaly (Greece) Zografos, K., Lancaster University (United Kingdom) Zopounidis, C., Technical University of Crete (Greece)
Organizing Committee
Kytagias, Ch., Piraeus University of Applied Sciences (Greece) Laskari, D., Piraeus University of Applied Sciences (Greece) Salmon, I, Piraeus University of Applied Sciences (Greece) Servos, D., Piraeus University of Applied Sciences (Greece) Toufexis, E., Piraeus University of Applied Sciences (Greece) Vasalakis, S., Piraeus University of Applied Sciences (Greece) Xanthopoulos, Th., Piraeus University of Applied Sciences (Greece)
Secretariat Mrs Georgia Mouriadou Hellenic Operational Research Society
5th International Symposium and 27th National Conference on Operation Research
Piraeus University of Appluied Sciences (Technological Education Institute of Piraeus)
Aigaleo - Athens on June 9-11, 2016
Page 5
Email: eeee [at] otenet.gr Tel: +30 210 3807532, Fax: +30 210 3807807
ISBN: 978-618-80361-6-1